f879c00f9d
# Description
There are times where explicitly specifying a schema for a dataframe is
needed such as:
- Opening CSV and JSON lines files and needing provide more information
to polars to keep it from failing or in a desire to override default
type conversion
- When converting a nushell value to a dataframe and wanting to override
the default conversion behaviors.
This pull requests provides:
- A flag to allow specifying a schema when using dfr into-df
- A flag to allow specifying a schema when using dfr open that works for
CSV and JSON types
- A new command `dfr schema` which displays schema information and will
allow display support schema dtypes
Schema is specified creating a record that has the key value and the
dtype. Examples usages:
```
{a:1, b:{a:2}} | dfr into-df -s {a: u8, b: {a: i32}} | dfr schema
{a: 1, b: {a: [1 2 3]}, c: [a b c]} | dfr into-df -s {a: u8, b: {a: list<u64>}, c: list<str>} | dfr schema
dfr open -s {pid: i32, ppid: i32, name: str, status: str, cpu: f64, mem: i64, virtual: i64} /tmp/ps.jsonl | dfr schema
```
Supported dtypes:
null
bool
u8
u16
u32
u64
i8
i16
i32
i64
f32
f64
str
binary
date
datetime[time_unit: (ms, us, ns) timezone (optional)]
duration[time_unit: (ms, us, ns)]
time
object
unknown
list[dtype]
structs are also supported but are specified via another record:
{a: u8, b: {d: str}}
Another feature with the dfr schema command is that it returns the data
back in a format that can be passed to provide a valid schema that can
be passed in as schema argument:
<img width="638" alt="Screenshot 2024-01-29 at 10 23 58"
src="https://github.com/nushell/nushell/assets/56345/b49c3bff-5cda-4c86-975a-dfd91d991373">
---------
Co-authored-by: Jack Wright <jack.wright@disqo.com>
83 lines
2.5 KiB
Rust
83 lines
2.5 KiB
Rust
use crate::dataframe::values::{Column, NuDataFrame, NuExpression};
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use nu_engine::CallExt;
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use nu_protocol::{
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ast::Call,
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engine::{Command, EngineState, Stack},
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Category, Example, PipelineData, ShellError, Signature, Span, SyntaxShape, Type, Value,
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};
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use polars::prelude::arg_where;
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#[derive(Clone)]
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pub struct ExprArgWhere;
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impl Command for ExprArgWhere {
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fn name(&self) -> &str {
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"dfr arg-where"
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}
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fn usage(&self) -> &str {
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"Creates an expression that returns the arguments where expression is true."
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}
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fn signature(&self) -> Signature {
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Signature::build(self.name())
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.required("column name", SyntaxShape::Any, "Expression to evaluate")
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.input_output_type(Type::Any, Type::Custom("expression".into()))
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.category(Category::Custom("expression".into()))
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}
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fn examples(&self) -> Vec<Example> {
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vec![Example {
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description: "Return a dataframe where the value match the expression",
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example: "let df = ([[a b]; [one 1] [two 2] [three 3]] | dfr into-df);
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$df | dfr select (dfr arg-where ((dfr col b) >= 2) | dfr as b_arg)",
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result: Some(
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NuDataFrame::try_from_columns(
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vec![Column::new(
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"b_arg".to_string(),
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vec![Value::test_int(1), Value::test_int(2)],
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)],
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None,
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)
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.expect("simple df for test should not fail")
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.into_value(Span::test_data()),
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),
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}]
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}
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fn search_terms(&self) -> Vec<&str> {
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vec!["condition", "match", "if"]
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}
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fn run(
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&self,
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engine_state: &EngineState,
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stack: &mut Stack,
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call: &Call,
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_input: PipelineData,
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) -> Result<PipelineData, ShellError> {
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let value: Value = call.req(engine_state, stack, 0)?;
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let expr = NuExpression::try_from_value(value)?;
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let expr: NuExpression = arg_where(expr.into_polars()).into();
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Ok(PipelineData::Value(expr.into_value(call.head), None))
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}
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}
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#[cfg(test)]
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mod test {
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use super::super::super::test_dataframe::test_dataframe;
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use super::*;
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use crate::dataframe::expressions::ExprAlias;
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use crate::dataframe::lazy::LazySelect;
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#[test]
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fn test_examples() {
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test_dataframe(vec![
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Box::new(ExprArgWhere {}),
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Box::new(ExprAlias {}),
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Box::new(LazySelect {}),
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])
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}
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}
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